This study is investigating how AI can help doctors outline the prostate on an ultrasound image to make a custom radiation plan during a specialized type of radiation treatment for prostate cancer called brachytherapy.

This is a Phase II prospective study evaluating the standard U-net, a deep learning AI algorithm for auto-contouring of the prostate during HDR prostate brachytherapy with the needles in place by new learners. Contouring will be done on TRUS. The study will be conducted with a randomized design. Each patient will be assigned to a new learner and then randomized to manual versus AI-assisted contouring. The randomization will be stratified by new learner type: resident versus fellow/new attending. The hypothesis is that AI-assisted learner contours will have improved Dice coefficients with respect to clinically approved contours compared with manual learner contours. All brachytherapy contours will undergo review by the treating radiation oncologist who is the experienced clinician for clinical approval prior to patient treatment. The experienced clinician will be blinded to the randomization.

Inclusion Criteria:

• 18 years of age and older
• Deemed suitable candidates for whole gland HDR prostate brachytherapy under general anesthesia as a monotherapy, boost or salvage treatment.

Exclusion Criteria:

• Prior permanent seed LDR brachytherapy implant
• Prior transurethral resection of the prostate (TURP)
• Presence or insertion of a rectal spacer
• Focal HDR brachytherapy treatment i.e. not whole prostate

The experienced clinician will be blinded to the randomization of the contouring method by the new learner. The prostate contour generated by the new learner will then undergo review by the experienced clinician.